Open-Set Recognition for Potato Leaf Disease Identification Using OpenMax

Traditional methods for identifying potato leaf diseases rely on manual visual inspection, which is prone to human error and inefficiency. While machine learning models have improved automation, conventional closed-set classifiers fail to recognize unknown diseases outside their training scope, limiting real-world applicability. This study addresses this gap by implementing Open-Set Recognition (OSR) using the OpenMax framework to classify known potato leaf diseases while effectively rejecting unknown pathologies. Leveraging the Xception architecture with dual learning schedulers (ReduceLROnPlateau and StepLR), we optimized OpenMax parameters, including distance metrics (Euclidean, Eucos) and rejection thresholds. After rigorous tuning, the model achieved 86.8% accuracy and 86.4% F1-score under an openness score of 18.3%, with optimal performance using Euclidean distance and a 0.95 threshold. The results demonstrate robust discrimination between known classes (potato late blight, early blight, healthy leaves) and visually similar unknown classes (e.g., tomato diseases, healthy bell peppers). This work enhances AI-driven agricultural diagnostics by bridging the gap between closed-set precision and open-set practicality, offering a scalable solution for real-world disease identification where novel pathogens may emerge.